{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "9.1. 图像增广.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chongzicbo/Dive-into-Deep-Learning-tf.keras/blob/master/9.1.%20%E5%9B%BE%E5%83%8F%E5%A2%9E%E5%B9%BF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gk5stHF6kJ5B",
        "colab_type": "text"
      },
      "source": [
        "##9.1. 图像增广\n",
        "在“深度卷积神经网络（AlexNet）”小节里我们提到过，大规模数据集是成功应用深度神经网络的前提。图像增广（image augmentation）技术通过对训练图像做一系列随机改变，来产生相似但又不同的训练样本，从而扩大训练数据集的规模。图像增广的另一种解释是，随机改变训练样本可以降低模型对某些属性的依赖，从而提高模型的泛化能力。例如，我们可以对图像进行不同方式的裁剪，使感兴趣的物体出现在不同位置，从而减轻模型对物体出现位置的依赖性。我们也可以调整亮度、色彩等因素来降低模型对色彩的敏感度。可以说，在当年AlexNet的成功中，图像增广技术功不可没。本节我们将讨论这个在计算机视觉里被广泛使用的技术。\n",
        "\n",
        "首先，导入实验所需的包或模块。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GX4Y1mD7sFee",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import losses,optimizers,Sequential,layers\n",
        "from tensorflow.data import Dataset\n",
        "from IPython import display\n",
        "from matplotlib import pyplot as plt\n",
        "import cv2\n",
        "import sys\n",
        "import time\n",
        "from tensorflow.keras.applications import vgg16"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4P1zm4Ys4nk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.enable_eager_execution()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S7fyVljFkZxr",
        "colab_type": "text"
      },
      "source": [
        "###9.1.1. 常用的图像增广方法\n",
        "我们来读取一张形状为400×500（高和宽分别为400像素和500像素）的图像作为实验的样例。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQNcjVfWs68d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def use_svg_display():\n",
        "  #用矢量图显示\n",
        "  display.set_matplotlib_formats('svg')\n",
        "def set_figsize(figsize=(3.5,2.5)):\n",
        "  use_svg_display()\n",
        "  #设置图的尺寸\n",
        "  plt.rcParams['figure.figsize']=figsize"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6ZyiO7BvBnm",
        "colab_type": "code",
        "outputId": "8f27444b-f904-4f90-e2ec-a287100f753f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJrZbDhVvTR9",
        "colab_type": "code",
        "outputId": "be3cd773-b28a-4aa2-c747-5e1a1388bd04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "set_figsize()\n",
        "img=cv2.imread('/content/drive/My Drive/data/d2l-zh-tensoflow/img/cat1.jpg')\n",
        "plt.imshow(img)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f046c9cc278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"170.607469pt\" version=\"1.1\" viewBox=\"0 0 210.3625 170.607469\" width=\"210.3625pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 170.607469 \nL 210.3625 170.607469 \nL 210.3625 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 146.729344 \nL 203.1625 146.729344 \nL 203.1625 10.829344 \nL 33.2875 10.829344 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p0e3a5608b2)\">\n    <image height=\"136\" id=\"imagedce2858718\" 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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ibG164CetHXC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_images(imgs,num_rows,num_cols,scale=2):\n",
        "  figsize=(num_cols*scale,num_rows*scale)\n",
        "  _,axes=plt.subplots(num_rows,num_cols,figsize=figsize)\n",
        "  for i in range(num_rows):\n",
        "    for j in range(num_cols):\n",
        "      axes[i][j].imshow(imgs[i*num_cols+j].numpy()/255)\n",
        "      axes[i][j].axes.get_xaxis().set_visible(False)\n",
        "      axes[i][j].axes.get_yaxis().set_visible(False)\n",
        "  return axes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffrLTykvk5L6",
        "colab_type": "text"
      },
      "source": [
        "大部分图像增广方法都有一定的随机性。为了方便观察图像增广的效果，接下来我们定义一个辅助函数apply。这个函数对输入图像img多次运行图像增广方法aug并展示所有的结果。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RoUhz1cvSi1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def apply(img,aug,num_rows=2,num_cols=4,scale=1.5):\n",
        "  Y=[aug(img) for _ in range(num_rows*num_cols)]\n",
        "  show_images(Y,num_rows,num_cols,scale)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GNlcqJ3RlAtB",
        "colab_type": "text"
      },
      "source": [
        "####9.1.1.1. 翻转和裁剪\n",
        "左右翻转图像通常不改变物体的类别。它是最早也是最广泛使用的一种图像增广方法。下面我们通过tf.image模块的flip_left_right方法来实现图像左右翻转。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hlP0RYuuvY4",
        "colab_type": "code",
        "outputId": "49cb9366-5246-49f6-ffb0-c2bb50d15645",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        }
      },
      "source": [
        "apply(img,tf.image.flip_left_right)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 8 Axes>"
            ],
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IaK0JIWDrCi0NMToqb3nru99gzY55/sMfpL/SY7C9w9ruNk888ySxt0lvZYXuqM/tvRk7j+1SVpZzWz0mswWoDk8/eZYfvH1EJzX8/Kc+wuvv7vOep8/zsGrx/JOXePrxLabLmvvv3qad54gYSXoDfOVReYtQVcyWM7Z3dzBa88x7308ZOzy6/xApIkEEXvjEZ/jkf/qf8fJDzfZjT5OmOX/29RdZLCx1SBj0FCvlTb7zr/8Prv3oxQYki4AvPZ1+i2Vhmc3nRG/RWhEJ2NoTFahRP70ahEJLjbWexEikVIToEDFFioil4sabr/OHX/w8P/yTr/Lhz/wyU5FxaXeNXtvQaRnOrCmWZHz0sRZzkTLoCqqYkmeGTkvw/WsTPvup93Jnf4LNV1jfucit/YpU1JzZWWG2tCS6w7J25K0erTyltoK83+WJy5eoS1gZtPFB0k4c6+urnIQOz7/wXs5dfp4q3+GkzhmuDglJSrvbx9WW2fGUw0d36U6v880vfZ5ZsUAgEFpTlJbt7S1ms4KqaBB7jAHnHMak+GBxFtTGavuqFJLKBjqdFCUVRnqETvEh4PEYYQgx4G0gLAte+tq/4jMf/wRp3qG/klGWDhugXgRCKkljYC2VVAJkEByMK773yh12z6xwf+ZJh6v8+OGYtkl55vkr3Ll1jyceP8+jOlLOF2SDLaLSFE6yvTNiZXWVditjTsLaxgqQ0cskWdbhey9eY23nPCjD/iwyW0hanRbBFZw9u0o/d/TihGvf+gqUARdBhkgMDXZaLmcN0ncWLyMqakASfMAJiZYBNexlV2MAZSRKp0gCta/xLkErj9bpqU4sQGkQIJC8/N3vs+g9RkGOsY5emrDSTVnRgtpHtJFMy8i9aeQv79egU557csTLb43p9Fp0Ohl7x0u63YzHLu1gpeLTn/wkr/z4Op3hOvfu3+LKM88TpWJte5W7tw9IWh0uXz7LZLLAVhVHM89ouIJUkaKItHtdhKt4eGcPv5zwxstvkLUzins/4vjRPSpfIbXBuZK6ikipqG0gVwpSdcpJG8DqvENqQIBaH6VXlVBoEVhUHhUjHoWSEIInhojRGXXtQAqssyzKkq2P/5eErEWytsneccHD45onz3QZzyq8TPjW9Tm3ZvDmu0d87JkVnj3T5UwHliLjk0/loAzjeeDpCyNOCo+RLdKOQUZJJ88ZrO6itedgf8zK7uMc7z/ksScvc/vmTYzRvHX7EUd7Dzmz1edw7xFCBfLM0O12OHtug7W1EZfPr7KYTDABFjawe/k5tnYvs3bhIkfHS8riCIVk4/xTrPY2OD7YQ+IxWpGYpiBqG1Cjbn5VaYEQBh8dQkiStOlPWiqiVFjrUFrhyopFUXHxU38b1Vtj+7En6W9sQpQ8PDzmwrBF6Wt2hjlSwtnVlO2OYL1lmJaw3pUkqSRLBbkUnBnlnB9pjhYRFz0X13NeefUWdZqxtnuR6XjOopJUdsH0aEySaaaLBXv3jhh0+7QzA0IwGq1y+dwW9x6ckGUp1sEgBxDsnD2DVpH1XoL0Eq8EUa6we24b0x7xa7/4WYZrW2xeOMs7117C2ogLARsi1luMSVDro/bV4Gn8Mymx0CiNSYqtIwiBc57EaOYONj/0N3nmhfezfvYs3X4H1enQPdWdQvB0Oy36nZQsUXgBRklyLWllkkXl8Ah8DaOWwHnZiPJSobWgWJa8+PotVnpDOmsDOnmC04KHd6b0NjY5Oh6D00yPZxRlwe6FMzhXI6QlishyMmOxcGS9HBEBoei2FcViDvWUVmvAcPsiN6+9zc996nl8Yfn+W7forAz58hf/eSMXKQkyEmLAKAUR1FpPXxVKYqRCSEVd11AHiuhQ4j/q3WXwyAufpTW8QMTQHrRYGW7gROTt28f0+znPPn2OZZRYG0FKahtBgouCwp+WrtAcVx4lJXMfEUKQJrCWa25O4PLFc0zrQGYkMXiUTjg4WrA4OkSHwPhkysXLZ4lJgjGCK88+ha8LQoBep8VsfEyr36bX7dDJFVVl+fFfXuPKs8+x1V+nXlZ4u82Xvvolzly+TFUGvv3Hv4vUEh9BKhAojFAI0dhsUugUIRMCGh89USWExKBCJMtyqlizN/GMXvh1upvb9DYGJCsrzAuHCjUDF7i01ePS+Q1uPpzQzxUoybRypIkglYJl7ZAhYF1k7hoX5nDhyLSgtg6cZ6kkSa/FyXSOl5qQ5Wxur9HOJZ1Om7w3ovKClZ0tKr/k7KUdWr2cO7ceEGMOSqCShNXNDQQ1ZVmyXJbcu3uX1koHUkXebvFg6XhUvMPjz7+P1cGI6WTRmAQRZITgBCEERDDIKFBI1KCXXBUIymCxDtJT4S1NDEezGficxz/x66hOi1ZvRNrKIE2AlCfP9tm5cIbV1R4rbUMVAk5KbOXZWU0Yl40EXNtIYiTdVDHKJXkmyLSmox3DtubRwtJpa966VbC11qPdkrQlaKOpVM7GaguLJ9iSzY0u589uMZ9OSNtDjscTFpMlo61VyumCGCu6JpKbigf3Dzk6OaZYluzubnF8POZ4PMEriS2OmZycsHl2lztv/RCNwBEhajKjsdI2FWVUw92cAOkCQTZyhowCVzWWz+7P/hplUDy2fQaRS7orq3idst7rs/fgkO6Zx+i2DCfjGTurHYa5IUjoaOh2NJ0EbghYzSQqelKtGApF5QOZkhgC7XbCUQHKRDyeVqYoAuSp4tJ6h2lbMTs5pqvX6bQkjw7meJVh9Qoh7iHSBO0jSSejb9rYyQEnxwXzRcH62gaTo0OOJjPifEmvb9g4d5Y3rkk+/MJ5XnnjXaToE1lidI2IIBJNXFi8DggbUf2WuSqlRAqBSjTBeZSwZEmLzed+gaS7Trc/worA7pk1JJK1YZ+tvOTa9Yf0NnZZXW3TV4rzqwlBAATWtCA10BKgtKKlA1JIRICugkRLnAeJpLSWad1QgV6akCWCWRnY6CXIAJbA7laf1LSo64gwCb3+CCMsRbkgljW9XCDwDFotHu3vc+ny43TaGcEHiumY2/eOeezJy7z9xnWWToOQrK8Pmc5r7l9/Da08eIGKitoFjBBEKfDeoTaGrasueoxMsa4kNzl1gN7u+3juQx9j/ewG/UHGqN/Hp12ErTg3yvmj/+8/sLa2zUSvsNprU9TQ6Z+mNRAEcbooKiIDzSEQBVIIMh1ZItgroDaCV27VDHJJJQQiRowSaBcYppJu23BURl67fkgrk9w5LFnOp2SthF47R7pAJkvSBLQAkxgWpWW+LMl7PSQli9LSao+YVyW766ukWcaiqCmt4/zGOk8/9R5efPHbCFEjpWiUTSUay8kF1NqgfVVrRZJC9IIoIE0zVp/6BGMLg2GX9eGALNGMugn9VPCVr/4JvkrJBkOGG9uMnWKn3yXPZUNjlKCdCCIBh6QMoJXA+4hWAikgEYJ5FCQSbh452kaSa91Ip6UleodOJDMHWIGraqaVoKMjWZqy0QEZDQ9uvcPB3h7eBiKR4viIxdKSmEioCg6Pxgx6LZIsY1HC3qM9Bmur2Nrz9MaI9+3ukmUZl5//KN/95jfQqulF3nnA4wlIpQMAZeEJUhMJPPb+T3Px6cdYWxvgnYKoGA1bFEXJS2/uMQmbPJoHxkdLzu/2ePbigGVc0NZAULSUQAWBRhJjxBJxtgF3JxW8Own42DghuYykiabTbnBVlibUUlCGJh9V1Z6T4wOWixMubHVIDZhE8s6Nh7z2+tsQArbW2AB2esLr194g0yUtI5hPjimXY1rtnPWdVR49fMD29hrTZcHAWZ5OJCuu4sr6Gu/bWuO///v/My40dn2MsQmyGYMa9vKr3gUQkeAc3Xaf4RMfptXOGK72SPM2lS1JteArf/R9KjNCKslwa4eHDw/53Kc/TCUshEi/k7KoQUqwMTAwEhsiSkiiABtgXAUcAkug8hFjFPenkKgmfYaIRAS3DyqWZcW7dw/IkxaTwyMsAh810S1pt7rcu7GPMnBhY4VWnDM+OASTUVpPP1dsrHVZlgXtzoDjsWNnc8jJrMRQkZiMA6s5ODjk1R/fYG4X/F+f/02kXRBjJISAURLrPdp5QZKkJAqc8+xPl6xMSla3BCF6+onHBcXX//RFpkWL4vZ9Lj/3FF5IRlubfOP7b/OZn7vCcVkyngeW1qGiJjUCCHS0JLpAHSWJhmkt2GhHcqPZnwWCAC0Ds7knzRUHJzXz8QnzQtHWOVIYfvjWPlujHkmas3c85vDBCVmvjWxFOrnm//39/4cr73kfRRB0soRHD/d4azbnQ+85R1tpvvfl3+LkeEZVzzHGEKxvQhRBgoj42qGUp6giRks8FmkSnA2gFGq1l1xVEkIE6z11MPTPPkXaapFnHWZlzfV3bnHr3SkFKUknZ3V3xGCQ0xqscO78GpPaUgVFdI5Q1LRTw9x7IprSwTJAYRtvniiYlxYhFUUlaMtACDDqGo5KR09qLJJiOSfLJMJ5kkzi64AicDyZ0+4Zzm2ukIaCd197gyc//DFUq0/SXuHVNx+yv39C7Sx3b93jwduvcfPGOzhfkqYptiqJwjcLICPeB9JUIpXBuYDSHu8EQkd0AGJERympQk2qFVEY2u0cpfKmH7hARNMfrcIgY3d1lUFSshvv0BcblK1VtlcMTz2xyVe/dYeV8318VNw6OEEKxUMJ652MKAW9xDBDoWRk6QT7D6eUtYPhCg5LVSs22wnX98e00hwRYO9kRstHUJJUlNy7PybYiuOH+4xVyaP7dzl/5TmW1ZL+sMNffP1HHB3M6A3WeOFn3kdVTxDiBZJWxo9e+i5VWZMkilQYpKgRUaFkgneOGD1SCbwVROHwThNFY2aqUS+7an2TBHMxcObic5x/7j30V3sM17qU5YwfvzOn1V4hpBmPncn5s3/xG5zceZO+KRG6xa2Z4cbdKR+9ssNkWVEvSrROmIyXBOsQRhCTDGcdM+epa1g6WG8bZnWN8YLJsibKQL0oSEXg/tGE5y+uc//OQ7q9FuPxmH5bcv3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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cx3loFFLlS6r",
        "colab_type": "text"
      },
      "source": [
        "上下翻转不如左右翻转通用。但是至少对于样例图像，上下翻转不会造成识别障碍。下面我们使用flip_up_down方法来实现图像上下翻转。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7eVDKBdcxWQe",
        "colab_type": "code",
        "outputId": "99e88fe4-a8bf-41bd-e4e5-74e67525a6b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        }
      },
      "source": [
        "apply(img,tf.image.flip_up_down)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 8 Axes>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"165.078814pt\" version=\"1.1\" viewBox=\"0 0 352.7 165.078814\" width=\"352.7pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 165.078814 \nL 352.7 165.078814 \nL 352.7 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 10.7 65.426087 \nL 83.482609 65.426087 \nL 83.482609 7.2 \nL 10.7 7.2 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#pba97493cf8)\">\n    <image height=\"59\" id=\"imagece12f8f8dc\" transform=\"scale(1 -1)translate(0 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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_azIDnQlksA",
        "colab_type": "text"
      },
      "source": [
        "在我们使用的样例图像里，猫在图像正中间，但一般情况下可能不是这样。在“池化层”一节里我们解释了池化层能降低卷积层对目标位置的敏感度。除此之外，我们还可以通过对图像随机裁剪来让物体以不同的比例出现在图像的不同位置，这同样能够降低模型对目标位置的敏感性。\\\n",
        "下面的代码里，使用resize_images方法和random_crop来实现图像的随机裁剪和缩放"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9i65OVeVxhWi",
        "colab_type": "code",
        "outputId": "ae8bfaee-7de3-4d3e-95d4-21d63e3352e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "#随机裁剪并resize\n",
        "num_rows=2\n",
        "num_cols=4\n",
        "scale=1.5\n",
        "shape=img.shape\n",
        "height=shape[0]//2\n",
        "width=shape[1]//2\n",
        "channels=shape[2]\n",
        "Y=[tf.image.resize_images(tf.image.random_crop(img,[height,width,channels]),size=(100,100)) for _ in range(num_rows*num_cols)]\n",
        "show_images(Y,num_rows,num_cols,scale)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f040e35ab70>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f040e307dd8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f040e2c43c8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f040e271978>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f040e227f28>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f040e1e2518>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f040e213ac8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f040e1d3080>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 8 Axes>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"179.635336pt\" version=\"1.1\" viewBox=\"0 0 352.7 179.635336\" width=\"352.7pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 179.635336 \nL 352.7 179.635336 \nL 352.7 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 10.7 79.982609 \nL 83.482609 79.982609 \nL 83.482609 7.2 \nL 10.7 7.2 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#pa351f9ba79)\">\n    <image height=\"73\" id=\"image6049949411\" transform=\"scale(1 -1)translate(0 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1GWsU1cjEOHJtOQyJWWWxaBlFRAhASpBRxBa88z4xrS1NFuYN1AKHCABDSLjKYKJlhWNqBds4JqsJU6cc1iPNbIqrKpbLFjeZzbCTCT4lNAc2uz1iapxmNn1gux/59KtfQDtDEdIYGYbMy8MDtRWeP79gvuyo2yVv377j3/nqK8J+y+XjM4Yx4Yxl1jnqpsP7QNAMmrG2LuV+2jFEz3LS4mpQaxEEbAHrmcAEi0FJAqa1kDONM2gug7lRhayYSjAWDgkshtbBtHF0FqIRDslirCEC2z4xO3HYKPQJsrF0tZJj4n5UJqslXWO4HyLu4uwMN5tys97ifWToPf2wxVQdXivaboqrLGMcaJoWtS3jEFitVpyeTEk+sZzN2RxGnj5+jAuJR4/PsQbUWOq6pjSspTSLLf9yUgTIOZNjxPtAVTeoCpPGEMZIVVk6KQvVa8aKoc2ABScQRTFGiFFxFowBK0KXFXGCEaiNwYgiRmiMYA34pDhTsK1pLckrNgTIAWcdi2lNFEEMaDa4+9sbPr88Z+8jQ3/P6ckFk+mU+92eh/cjYi0pRpbLFcYYXLvkdDnh5uaGFy82fPz0kuFwQNSyWswYxi3z6SmH/R5jLdiMqSqsVYwajHOkIzVTV46k0EmLNBXWBmrjyCkxnzk6gUqP1UwsVhWlkPsOyqoABqidwQBWIAlMHAQBEagQBjJWBGchprIAAGIBgdN5zcwpoqURFetQhZf3PW61PGEcE52rkZMlm80BRcFYlrMpnshsPiPngLXCYYRubKiaKRHLq/WAiz2/+OpLdsOBR5dn9OOAqRpaK9isNCpMasdhLPgjWUGUrnNErwQr7IaBeduxPmRaB8sO9iNEC/uYQQxRhazKqs5YADWMKYGxiJZosQrOKjlmWmtoECqBmQVF0FTIOSMJxJCj0AKSFQf0Y+bQwyg9lXOE7YDp93uUVNSEDN10St+PNFVFO2mYLRbstlsW8w6TlbZuuL5ZI2IQhH3fM3phOx6Yz5YcdiO7h5G76y0pRbIVpDZYyTTGUuVMZQ21LbihRvCambgKyYmuNrQC6uEhwIMP9MCdj9wMnk0KRMr2OwFjBaEIAlYBBatSCpApz/kw3qgqzoBHqa2lxeBMYQdUhJhgzIbtGAjJ4MTxMHpMyJHdfuT6foNRoT9sqBpHyJ5xPBC9xxqIMTKdTVjMKqbzlmHoGX2km0yo5zMcltmk5dXdmvWY2KeRbCuSEVpjSClhVHHWUlsLIhgp1ckdvw+akZRpKsvdLnG18WyjMgIRQbFkLSBujmlmtTx++JMBobxu+V7Jx/9PKgT98LySclYAUbBlMRXBh0z0SozKMGbcvh+YW0sznyA5UTsYQqBSx8Wqo2oWBONIQ2R1viImCN5z+dET3r65Z7Pt2ZvMAceb2wfEdux8ZloZhjGx6mqslCo3kqhEIQeIhuyUHCKCIefEZFKIsqtdYH2IzLqOqgFnlKo1DAFUywKJaAFVsSQBpTSLIoIxhVYJqqgRlIxiCCiSBMrfIjNpZswQjtcwJksOiYMfcVYwVjD7o9zSVhVDiOwOBzTDbrfD9wNvX7/CkBFVVJWcRpaLlnHosUA77Wi6hkos9+sdTVUhGulmS5rakMgEa4EiGuQYSDlispJiYgyl16qMwSgIhtEXLmg/DGVYlpIqKSWyKiKKRchASEpWQMsCqUBWiFlJpvTi5ecZY0prYbREUFnAwkWFqGS1aNaC0c2MwxjpfcBksbz94Q39+p6umZCCsN+MrB/2vH23BjGsb27JaWRz94BLAye1IfYjxgpLp3xxucRapZvPmbaWqbX43T1NDDhVDvuIPaZXZYTGgMmJEBJJi8LROkMryqQqkVCJIk2FqQwGxWmmcYXCrRES4LWUfFBUYdRcNkNhzEoftSwiQmcNlkxtoJVMa6CSfLwupZFCqWzHSBgC37++47t3O6auwlhrubq6RiUy7B+YzRZkLHf3e+azReljVNlt9+z3PQ+3G15894IUAjln2mmHccJi2tHazOgHurajrYQxBlQzQ0zEGJGkIEfh0xpSLt9XVUVbWayCSkSkpBQ5Y1EqK3RGmDjD1BkySgCGmMhaoqlEWon4lDNoKUQpK2gZU8p2cMQrLQKnamkp7BHjjCNoohblyemMaWswt9c3hEPP0PekFLm5v2e92VJXHf1hTVtbXFVzv96xPwxkHNs+MJ8tEBWsBvw40NZlIBzHEY09i0lFihkfI8YKlavIUsDz0HusE/oUqZxDj/1Pn4XaCrO6QnOpWo0RFGVUQ4hgUgHioMJDr6xDJqQjl20cORetX0TIUjApixIBTYXAEzFgoEXJUYihPKerHW1jcNOGs5MJq5lj0lWY1aJls17zq//zz7j65jtO51O++PwJs6mlbmegllcvXyEh8urNOzY+ot20dLsNBLXECDEk4hg46Swmjty+u2fMgldhs96x9Zn9mLndJ9Zj4ZLmswnzSUXjIJFRAz4Vkswetz6lIlTknAmaCEZYe+H7h8g+OtY9bENAFYak7LPSGqF20FhDJWBSRo6znhXoc0ZVOCQ4HPHLqVABcRxZzRpca1FJLDqDcabhi599yWc/+Yyr63c4jdg80FaBx4/POYwDztY8rLcs2ikY4fz0BCtKDJl9VFSF7DNjiqjPRcoZy+A8jhEfDW/XOw4hs/OJbGsCgpCoC99POjo8Yi4YYo6g24dcyrpIIddE2IVMEiGqEhEwjqCQkiJiqKzg9EN7UBbbSFkgVY5MgZJUUIV0TNkMiHFlc8hkFO8j5m6z5/3dA0NQnjz9mOt3r8EPLNqWfrdmtZxTCQxDYH23x7maFALDfo8Vw2a3R51js94TQ+b19ZpNHzh4ZT941g8H3l3fcVDLtk/4JNzt9ow5l4E5Fsq4H5UoJY1iVrJA9OVGCqWRCSkVfBPKWGFyoXi19EFVbRAgqlI5cObve7F8XKykEArTTEgwjJkxQpLiONGUyjRQWawoj07PcHH0qFoO48j+4Bn8jvuHB5xz/Pv/8T/i3d2GuracnK7o1aBimC9WrNf37PuBGA2v3m9ZdPDockU9O+M+KofDQBiExhniGNEsjDFzt+25PFlxv+6ZOktSy3afyCYhdYdLGe8TQYV+iOxGx64pPPeHSJo5aEwmJkMfYDsmFp1ipQgEMSsSMxiDGCGpMJqMZhiTEEJRTYyBoIaQEkimlooEWFsznRjmjePXf/sad3a6ZDKfkDWx3QSenX7Ci+9/R9NWvPj2a5aPnlHPDDpkbN0g1vDm+o6clOgzuIqqbbCtxXtP8gqVpU8RHnZYlMvlBKPgqhrNAylmBu/xIWMt7AfPbOpo5RhFmB8HU0ux7+1CxhqDU6Ez4FxJpJgzlbPknLDOEJEyx4mQckbFoqqEJMdUK79jjStjSEwAaBaiKIOPZDWknNkdRny0OFO3RGqmswled8xPzvms7qiM5frdKxDD6Sdfsh08YUyIabjZbjEimCB0kqkJJG84uTwh+B2GiLWZup0QDgeGPnB7e0fXLTgcet7FgaatIWV22wMpg6gtHXM2aEhAYtJZFCWK4NVhfKIT5aCFCjnEhBqDpgxSok1TqWTWCUEgRWgqYchKiJT3MoYgGYNhiEWPM2I5hMgQM0oipUIFb/cHrJz9/i/nj54wZkM7W9IHZXlygY+Gdj7n9uqWaZMRMrPlkqfPHnGymLJaLdgNmX4YEWd4fHZCjgPeR3aHkcenC4yxGI1MphVnqwXb/Y4YhZgFMYZ5U6MxMW1bRmeZNJZ5LcQQyTnTVMLMFnwYQ8EwjGCSosmQSXSNwyZlUhUZ6hChT0rEMKTCcyPQh1IIdmNgjIW20ahs+0BlShsyJmUYInuvzNqGkB0Bxa0f9txcb7l8dArWYZywG3rarqZbnGCrhpfffs+jTz5m9bQhDHtc3aEknDkwmXYEhap2Rfloa0LM5JxBIvPZhM4JfhhxleV0ZtiNA/NFh3XCtG6oKkskEbOhqg1dbUk54TRTGYekXMYYLDFDU1kOYybmRAjK1NWEXMaOLAW4jX7Q7BKCxYeivoxHCtcKaMxYhMP+gK0ce+/pfUDFsdkeuN/t6KzDtk/+8Je+jzwMiaZbcHK5pJvNoHKkLNhJA7Tc39yho0eMcDpp+Kv/58857DIHH+imLZ8/PS/A3+8Qa4koLab4nnIkI+wOIzoMnC8XDP3AbN6gxhANzGpDUwmVmGMHbOmamtYYYiwz1iEmxhjxuwG1jpiP1IgpVEfSUi0lC9Ya+qDEMTL6yM0hsB8yhyGQszCMnrv1A7vdQFW15AT7IeBzxiehm0/59NkKGXtM283JRgh95Pr6hqbuqCrHtGsREeqqpp22PPv4GTc3N9xdXfP69Wt8DNzd3rJczIjjgB977u8eCnvpihw9DoFaTPFNZ+gRRhVudiObLHRtS1VV7MYBNFFJwYyUEmKUMcHOR0YVQiqKLSIcMOzGCCrUVrHWFMBXCCnC8b0PfSBmZfSZ/ejZHQaGMdGPoQzWCFktgw/E7BEDkpTgFWOFeS3Fx3BydooYh1XlsN6z2+2KWqKJYRjodz2TScMYRiazloe7W3744QeMOMLQE/YHJEb6cWTrPSklGEemXcuogQqhEUMthqenJ6jNZBKtNYQwEKInp8yibZCsjCGX1EiF3xlCZIiR9XaHD4EcM3eHwHYIhYsyQs6plPkQC28EqHGMY6AfE0GhHxPD6AkhMA6ewcdiuzmyCdYJk65iHEYGr6gIFqWpLe4nX33K25d3bLcPWOvY3Afaas/paoIOI7k2XC7nvNgkFqsld9c3OIRpZ3kXI7/59d/w/PlHrC9OOZk79v2epu0Iw5756YoYA2Tl2cmUxsG2qdl5WNkyl4mFbAxoJGbYjR4fM4vagIyEo3uuaWumpjSGQ4Bl15LVsA9gXekTQgiILX4mnxNK5hAUPyY2tweaxmGt0FUVIURCSmRAK+FyMmf9sAM1/PDmmiQB3U/Zvvgac3Gx5Px8yXSxYL6cILYMidv1A7UNNCRmDTx/smT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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJCQDYHCl6Wa",
        "colab_type": "text"
      },
      "source": [
        "####9.1.1.2. 变化颜色\n",
        "另一类增广方法是变化颜色。我们可以从4个方面改变图像的颜色：亮度、对比度、饱和度和色调。在下面的例子里，我们将调整图像的亮度"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-rT5TdJn4lEL",
        "colab_type": "code",
        "outputId": "6e6cb258-ddd4-43c2-efe0-5526f2bbedc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "#随机调整图片亮度\n",
        "Y=[tf.image.random_brightness(img,max_delta=0.2) for _ in range(num_rows*num_cols)]\n",
        "show_images(Y,num_rows,num_cols,scale)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f040e35a860>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f040e156b00>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f040e1130f0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f040e0c36a0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f040e074c50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f040e033240>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f03f87c37f0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f03f87f3d68>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 8 Axes>"
            ],
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pk0hTl1AmIr8",
        "colab_type": "text"
      },
      "source": [
        "类似地，我们也可以变化图像的色调"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1qoR54NB9mi",
        "colab_type": "code",
        "outputId": "c4bedf9e-39eb-4988-a9bb-56e7f862765e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        }
      },
      "source": [
        "#随机变化图像的色调\n",
        "Y=[tf.image.random_hue(img,max_delta=0.2) for _ in range(num_rows*num_cols)]\n",
        "axes=show_images(Y,num_rows,num_cols,scale)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 8 Axes>"
            ],
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vRfdc4aCVys",
        "colab_type": "text"
      },
      "source": [
        "###9.1.2. 使用图像增广训练模型\n",
        "下面我们来看一个将图像增广应用在实际训练中的例子。这里我们使用CIFAR-10数据集，而不是之前我们一直使用的Fashion-MNIST数据集。这是因为Fashion-MNIST数据集中物体的位置和尺寸都已经经过归一化处理，而CIFAR-10数据集中物体的颜色和大小区别更加显著。下面展示了CIFAR-10数据集中前32张训练图像。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5jXBwF8CmbIg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "7f16edbc-76f9-4aa9-efe9-c4a453ee64d7"
      },
      "source": [
        "(x_train,y_train),(x_test,y_test)=keras.datasets.cifar10.load_data()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 6s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JNTlZJOmvpA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b4d4b6a0-5c62-42e5-cb89-f98e1d055d81"
      },
      "source": [
        "x_train.shape,y_train.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((50000, 32, 32, 3), (50000, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3mKPLlQUm6bK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "outputId": "41183901-2b24-410c-f13c-3d04bd18ba63"
      },
      "source": [
        "axes=show_images(tf.constant(x_train[0:32]),4,8,scale=0.8)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 460.8x230.4 with 32 Axes>"
            ],
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          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BAz2he5pnoXM",
        "colab_type": "text"
      },
      "source": [
        "为了在预测时得到确定的结果，我们通常只将图像增广应用在训练样本上，而不在预测时使用含随机操作的图像增广。在这里我们只使用最简单的随机左右翻转，并将图像像素值变换为在0到1之间且类型为32位浮点数。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BqRKdRsHoYrr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataset=Dataset.from_tensor_slices((x_train,y_train)).shuffle(10000).map(lambda x,y:(x/255,y)).batch(256).map(lambda x,y:(tf.image.random_flip_left_right(x),y)) #训练数据\n",
        "test_dataset=Dataset.from_tensor_slices((x_test,y_test)).map(lambda x,y:(x/255,y)).batch(256) #训练数据"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVIkFaH3p0bZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "24d4bf7b-d47f-4e2c-c1ae-f7bd5e0c14bd"
      },
      "source": [
        "for x,y in train_dataset:\n",
        "  # print(x.shape,y.shape)\n",
        "  print(x.shape)\n",
        "  print(y[0].shape)\n",
        "  break"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(256, 32, 32, 3)\n",
            "(1,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WuhAUBEiqskE",
        "colab_type": "text"
      },
      "source": [
        "9.1.2.1.训练模型\n",
        "下面使用随机左右翻转的图像增广来训练模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omotVAXDrL5x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer=optimizers.Adam() #优化器\n",
        "loss=losses.SparseCategoricalCrossentropy(from_logits=True) #损失函数"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0E3sdFu_OO7h",
        "colab_type": "text"
      },
      "source": [
        "我们构建一个简单的卷积神经网络来训练"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJ0ZQgCPxeG2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net=keras.Sequential([\n",
        "  layers.Conv2D(filters=32,kernel_size=3,strides=1,padding='same',activation='relu'),\n",
        "  layers.Conv2D(filters=32,kernel_size=3,strides=1,padding='same',activation='relu'),\n",
        "  layers.MaxPool2D(pool_size=(2,2)),  \n",
        "  layers.Conv2D(filters=64,kernel_size=3,strides=1,padding='same',activation='relu'),\n",
        "  layers.Conv2D(filters=64,kernel_size=3,strides=1,padding='same',activation='relu'),\n",
        "  layers.MaxPool2D(pool_size=(2,2)), \n",
        "  layers.Flatten(),\n",
        "  layers.Dense(512),\n",
        "  layers.Dropout(0.4),\n",
        "  layers.Dense(10,activation='softmax'),                    \n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dlffyr23YvMm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "eeb6dc9e-44e0-49fc-bd97-166a1b2575c4"
      },
      "source": [
        "net.compile(optimizer=optimizer,loss=loss,metrics=['accuracy'])\n",
        "net.fit_generator(train_dataset,validation_data=test_dataset,epochs=30)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 2.0997 - acc: 0.2889Epoch 1/30\n",
            "196/196 [==============================] - 13s 66ms/step - loss: 2.0988 - acc: 0.2899 - val_loss: 2.0051 - val_acc: 0.4589\n",
            "Epoch 2/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.9939 - acc: 0.4523Epoch 1/30\n",
            "196/196 [==============================] - 9s 47ms/step - loss: 1.9939 - acc: 0.4525 - val_loss: 1.9780 - val_acc: 0.4794\n",
            "Epoch 3/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.9315 - acc: 0.5115Epoch 1/30\n",
            "196/196 [==============================] - 9s 47ms/step - loss: 1.9315 - acc: 0.5116 - val_loss: 1.9062 - val_acc: 0.5513\n",
            "Epoch 4/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.8740 - acc: 0.5707Epoch 1/30\n",
            "196/196 [==============================] - 9s 48ms/step - loss: 1.8736 - acc: 0.5709 - val_loss: 1.8462 - val_acc: 0.6137\n",
            "Epoch 5/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.8233 - acc: 0.6306Epoch 1/30\n",
            "196/196 [==============================] - 9s 47ms/step - loss: 1.8230 - acc: 0.6307 - val_loss: 1.8113 - val_acc: 0.6455\n",
            "Epoch 6/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.8045 - acc: 0.6477Epoch 1/30\n",
            "196/196 [==============================] - 9s 47ms/step - loss: 1.8042 - acc: 0.6478 - val_loss: 1.7845 - val_acc: 0.6744\n",
            "Epoch 7/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.7840 - acc: 0.6713Epoch 1/30\n",
            "196/196 [==============================] - 9s 47ms/step - loss: 1.7835 - acc: 0.6714 - val_loss: 1.7826 - val_acc: 0.6792\n",
            "Epoch 8/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.7703 - acc: 0.6872Epoch 1/30\n",
            "196/196 [==============================] - 9s 47ms/step - loss: 1.7699 - acc: 0.6872 - val_loss: 1.7719 - val_acc: 0.6895\n",
            "Epoch 9/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.7497 - acc: 0.7021Epoch 1/30\n",
            "196/196 [==============================] - 9s 47ms/step - loss: 1.7494 - acc: 0.7021 - val_loss: 1.7754 - val_acc: 0.6865\n",
            "Epoch 10/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.7455 - acc: 0.7091Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.7453 - acc: 0.7092 - val_loss: 1.7526 - val_acc: 0.7087\n",
            "Epoch 11/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.7323 - acc: 0.7185Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.7318 - acc: 0.7186 - val_loss: 1.7435 - val_acc: 0.7173\n",
            "Epoch 12/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.7204 - acc: 0.7385Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.7201 - acc: 0.7385 - val_loss: 1.7403 - val_acc: 0.7207\n",
            "Epoch 13/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.7159 - acc: 0.7362Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.7154 - acc: 0.7364 - val_loss: 1.7278 - val_acc: 0.7328\n",
            "Epoch 14/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.7101 - acc: 0.7456Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.7097 - acc: 0.7456 - val_loss: 1.7279 - val_acc: 0.7315\n",
            "Epoch 15/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.7078 - acc: 0.7481Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.7073 - acc: 0.7482 - val_loss: 1.7313 - val_acc: 0.7291\n",
            "Epoch 16/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.7019 - acc: 0.7577Epoch 1/30\n",
            "196/196 [==============================] - 9s 48ms/step - loss: 1.7013 - acc: 0.7577 - val_loss: 1.7326 - val_acc: 0.7262\n",
            "Epoch 17/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.6945 - acc: 0.7642Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.6939 - acc: 0.7643 - val_loss: 1.7249 - val_acc: 0.7356\n",
            "Epoch 18/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.6924 - acc: 0.7645Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.6918 - acc: 0.7646 - val_loss: 1.7251 - val_acc: 0.7355\n",
            "Epoch 19/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.6952 - acc: 0.7624Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.6948 - acc: 0.7625 - val_loss: 1.7161 - val_acc: 0.7413\n",
            "Epoch 20/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.6886 - acc: 0.7725Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.6881 - acc: 0.7725 - val_loss: 1.7213 - val_acc: 0.7410\n",
            "Epoch 21/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.6875 - acc: 0.7725Epoch 1/30\n",
            "196/196 [==============================] - 10s 50ms/step - loss: 1.6871 - acc: 0.7725 - val_loss: 1.7191 - val_acc: 0.7432\n",
            "Epoch 22/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.6825 - acc: 0.7799Epoch 1/30\n",
            "196/196 [==============================] - 9s 47ms/step - loss: 1.6822 - acc: 0.7798 - val_loss: 1.7291 - val_acc: 0.7300\n",
            "Epoch 23/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.6830 - acc: 0.7688Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.6824 - acc: 0.7689 - val_loss: 1.7113 - val_acc: 0.7480\n",
            "Epoch 24/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.6811 - acc: 0.7805Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.6808 - acc: 0.7805 - val_loss: 1.7178 - val_acc: 0.7442\n",
            "Epoch 25/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.6775 - acc: 0.7835Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.6769 - acc: 0.7835 - val_loss: 1.7132 - val_acc: 0.7459\n",
            "Epoch 26/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.6733 - acc: 0.7883Epoch 1/30\n",
            "196/196 [==============================] - 9s 45ms/step - loss: 1.6728 - acc: 0.7883 - val_loss: 1.7133 - val_acc: 0.7470\n",
            "Epoch 27/30\n",
            "195/196 [============================>.] - ETA: 0s - loss: 1.6778 - acc: 0.7801Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.6772 - acc: 0.7801 - val_loss: 1.7147 - val_acc: 0.7454\n",
            "Epoch 28/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.6728 - acc: 0.7853Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.6723 - acc: 0.7854 - val_loss: 1.7105 - val_acc: 0.7503\n",
            "Epoch 29/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.6741 - acc: 0.7864Epoch 1/30\n",
            "196/196 [==============================] - 9s 47ms/step - loss: 1.6740 - acc: 0.7864 - val_loss: 1.7162 - val_acc: 0.7450\n",
            "Epoch 30/30\n",
            "194/196 [============================>.] - ETA: 0s - loss: 1.6723 - acc: 0.7874Epoch 1/30\n",
            "196/196 [==============================] - 9s 46ms/step - loss: 1.6718 - acc: 0.7874 - val_loss: 1.7073 - val_acc: 0.7525\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f03f586a390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5F9R0pBuZB4K",
        "colab_type": "text"
      },
      "source": [
        "9.1.3. 小结\n",
        "* 图像增广基于现有训练数据生成随机图像从而应对过拟合。\n",
        "* 为了在预测时得到确定的结果，通常只将图像增广应用在训练样本上，而不在预测时使用含随机操作的图像增广。\n",
        "* 可以从tf的image模块中获取有关图片增广的方法。"
      ]
    }
  ]
}